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Semi-automated Optical Heartbeat Analysis of Small Hearts
Published on: September 16, 2009
Weakly supervised video-based cardiac detection for hypertensive cardiomyopathy
Jiyun Chen1, Xijun Zhang1, Jianjun Yuan1
1Department of Ultrasonography, Henan Provincial People's Hospital, Zhengzhou, 450003, China.
Insights
This study introduces a video-based deep learning method for detecting hypertensive cardiomyopathy using echocardiograms. The AI model achieved high accuracy, offering a potential tool to assist clinicians in diagnosing cardiac disease.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Imaging
Background:
- Manual detection of cardiac disease from echocardiograms is labor-intensive and requires expertise.
- Clinical parameters like ejection fraction and strain are vital but their manual assessment is challenging.
- Hypertensive cardiomyopathy (HTCM) detection often relies on these complex manual analyses.
Purpose of the Study:
- To evaluate a novel video-based deep learning method for automated hypertensive cardiomyopathy detection.
- To assess the efficacy of an end-to-end deep learning pipeline using echocardiographic videos.
- To compare the performance of video-based versus image-based deep learning approaches in cardiac diagnostics.
Main Methods:
- Developed an end-to-end video-based deep learning pipeline utilizing 3D Convolutional Neural Networks (CNNs).
- Employed a weakly-supervised temporally correlated feature ensemble and a domain adversarial neural network to handle video variability.
- Trained and tested the model on 297 subjects (185 HTCM patients, 112 controls) using four apical chamber echo views.
Main Results:
- The video-based deep learning model achieved 92% accuracy, 0.90 AUC, 97% sensitivity, and 84% specificity for HTCM detection.
- The proposed method outperformed a standard 3D CNN (vanilla I3D) on key metrics.
- Video-based methods demonstrated superiority over image-based methods in integrating spatial and temporal echocardiographic information.
Conclusions:
- The study validates the potential of end-to-end video-based deep learning for automated echocardiographic diagnosis of hypertensive cardiomyopathy.
- This AI approach can augment clinical decision-making and assist healthcare professionals.
- The findings pave the way for more efficient and accurate cardiac disease detection using deep learning.
Introduction:
Parameters, such as left ventricular ejection fraction, peak strain dispersion, global longitudinal strain, etc. are influential and clinically interpretable for detection of cardiac disease, while manual detection requires laborious steps and expertise. In this study, we evaluated a video-based deep learning method that merely depends on echocardiographic videos from four apical chamber views of hypertensive cardiomyopathy detection.
Methods:
One hundred eighty-five hypertensive cardiomyopathy (HTCM) patients and 112 healthy normal controls (N) were enrolled in this diagnostic study. We collected 297 de-identified subjects' echo videos for training and testing of an end-to-end video-based pipeline of snippet proposal, snippet feature extraction by a three-dimensional (3-D) convolutional neural network (CNN), a weakly-supervised temporally correlated feature ensemble, and a final classification module. The snippet proposal step requires a preliminarily trained end-systole and end-diastole timing detection model to produce snippets that begin at end-diastole, and involve contraction and dilatation for a complete cardiac cycle. A domain adversarial neural network was introduced to systematically address the appearance variability of echo videos in terms of noise, blur, transducer depth, contrast, etc. to improve the generalization of deep learning algorithms. In contrast to previous image-based cardiac disease detection architectures, video-based approaches integrate spatial and temporal information better with a more powerful 3D convolutional operator.
Results:
Our proposed model achieved accuracy (ACC) of 92%, area under receiver operating characteristic (ROC) curve (AUC) of 0.90, sensitivity(SEN) of 97%, and specificity (SPE) of 84% with respect to subjects for hypertensive cardiomyopathy detection in the test data set, and outperformed the corresponding 3D CNN (vanilla I3D: ACC (0.90), AUC (0.89), SEN (0.94), and SPE (0.84)). On the whole, the video-based methods remarkably appeared superior to the image-based methods, while few evaluation metrics of image-based methods exhibited to be more compelling (sensitivity of 93% and negative predictive value of 100% for the image-based methods (ES/ED and random)).
Conclusion:
The results supported the possibility of using end-to-end video-based deep learning method for the automated diagnosis of hypertensive cardiomyopathy in the field of echocardiography to augment and assist clinicians.
Trial Registration:
Current Controlled Trials ChiCTR1900025325, Aug, 24, 2019. Retrospectively registered.
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